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recurrent_keras_power.py
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recurrent_keras_power.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Sep 20 14:53:25 2016
@author: Jhy1993
README:
LSTM a try/except statement so that we can interrupt the training without losing everythin to a KeyboardInterrupt.
In case of keyboard interruption, we return the model, y_test and X_test. The latter is returned so that you can run predict on the early-returned model if you like.
https://github.com/Vict0rSch/deep_learning/tree/master/keras/recurrent
"""
import matplotlib.pyplot as plt
import numpy as np
import time
import csv
from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
from keras.models import Sequential
np.random.seed(1234)
def data_power_consumption(path_to_dataset,
sequence_length=50,
ratio=1.0):
max_values = ratio * 2049280
with open(path_to_dataset) as f:
data = csv.reader(f, delimiter=";")
power = []
nb_of_values = 0
for line in data:
try:
power.append(float(line[2]))
nb_of_values += 1
except ValueError:
pass
# 2049280.0 is the total number of valid values, i.e. ratio = 1.0
if nb_of_values >= max_values:
break
print "Data loaded from csv. Formatting..."
result = []
for index in range(len(power) - sequence_length):
result.append(power[index: index + sequence_length])
result = np.array(result) # shape (2049230, 50)
result_mean = result.mean()
result -= result_mean
print "Shift : ", result_mean
print "Data : ", result.shape
row = round(0.9 * result.shape[0])
train = result[:row, :]
np.random.shuffle(train)
X_train = train[:, :-1]
y_train = train[:, -1]
X_test = result[row:, :-1]
y_test = result[row:, -1]
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
return [X_train, y_train, X_test, y_test]
def build_model():
model = Sequential()
layers = [1, 50, 100, 1]
model.add(LSTM(
input_dim=layers[0],
output_dim=layers[1],
return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(
layers[2],
return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(
output_dim=layers[3]))
model.add(Activation("linear"))
start = time.time()
model.compile(loss="mse", optimizer="rmsprop")
print "Compilation Time : ", time.time() - start
return model
def run_network(model=None, data=None):
global_start_time = time.time()
epochs = 1
ratio = 0.5
sequence_length = 50
path_to_dataset = 'household_power_consumption.txt'
if data is None:
print 'Loading data... '
X_train, y_train, X_test, y_test = data_power_consumption(
path_to_dataset, sequence_length, ratio)
else:
X_train, y_train, X_test, y_test = data
print '\nData Loaded. Compiling...\n'
if model is None:
model = build_model()
try:
model.fit(
X_train, y_train,
batch_size=512, nb_epoch=epochs, validation_split=0.05)
predicted = model.predict(X_test)
predicted = np.reshape(predicted, (predicted.size,))
except KeyboardInterrupt:
print 'Training duration (s) : ', time.time() - global_start_time
return model, y_test, 0
try:
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(y_test[:100])
plt.plot(predicted[:100])
plt.show()
except Exception as e:
print str(e)
print 'Training duration (s) : ', time.time() - global_start_time
return model, y_test, predicted